IP Library › Granted Patent US 12,285,820
Granted Patent B2
US 12,285,820 · App. 18/887,096 · Granted Apr 29, 2025

Method, control unit and laser cutting system for combined path and laser process planning for highly dynamic real-time systems

Inventors: Markus Steinlin (Zürich, CH); Titus Haas (Zofingen, CH)
Assignee: BYSTRONIC LASER AG
B23K26/38B23K26/03G05B19/182B23K2101/06B23K2101/18G05B2219/36199
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,285,820
App. No.
18/887,096
Granted
Apr 29, 2025
Kind
B2
Abstract

A control unit for calculating a spatially and time-resolved, combined setpoint data set for control of a laser cutting process includes a measurement data interface for accessing sensor data during the cutting operation, a process interface to a first memory that stores a process model that estimates status data of the laser cutting process and a cutting result, a machine interface to a second memory in which a machine model is stored which represents a kinematic behaviour of the laser cutting head and estimates status data of a movement process and the cutting result thereof, and a processor that executes an algorithm that couples the process model and the machine model via a feed rate value and/or via a nozzle spacing value. The processor accesses the process model and the machine model in order to calculate the spatially and time-resolved, combined setpoint data set with coordinated setpoints.

Claims (44)

1. A method for calculating a spatially and time-resolved, combined setpoint data set for open- and/or closed-loop control and performing a laser cutting process of a laser cutting machine with a laser cutting head during laser cutting of metal sheets or tubes, the method comprising:

measuring sensor data during the laser cutting process, wherein the sensor data encode a measured cutting result of the laser cutting process;

providing a process model stored in a first memory which represents the laser cutting process and estimates first status data of the laser cutting process and a cutting result resulting therefrom, wherein the first status data of the laser cutting process include physical laser parameters during laser cutting, wherein the physical laser parameters at least include a feed rate value for the laser cutting head and/or a nozzle spacing value;

providing a machine model stored in a second memory which represents a kinematic behavior of the laser cutting head during movement thereof and estimates second status data of a movement process and the cutting result resulting therefrom, wherein the second status data of the movement process at least include the feed rate value for the laser cutting head and/or the nozzle spacing value;

wherein the process model and the machine model are coupled via the feed rate value for the laser cutting head and/or via the nozzle spacing value;

accessing the process model in the first memory and the machine model in the second memory by a control unit in order, on a basis of the estimated first status data of the laser cutting process and the second status data of the movement process, to calculate the spatially and time-resolved, combined setpoint data set with coordinated setpoints for the laser cutting process and setpoints for the movement process, taking into account the measured sensor data;

comparing model-estimated cutting results from the process model and the machine model with the measured cutting result to identify deviations, wherein the process model and/or the machine model is updated and the spatially and time-resolved, combined setpoint data set is recalculated at least in an event of deviations; and

performing an open and/or closed loop control of the laser cutting process to reduce the deviations.

2. The method according to claim 1 , wherein the method further comprises:

acquiring a target input entered on a user interface for calculating a cost function, on the basis of which the combined spatially and time-resolved setpoint data set is calculated, the target input comprising several interdependent inputs, including a cutting quality input, a cutting operation robustness input and a productivity input.

3. The method according to claim 1 , wherein the combined spatially and time-resolved setpoint data set includes setpoint values for direct process variables and/or setpoint values for indirect process variables.

4. The method according to claim 3 , wherein the direct process variables comprise cutting speed, acceleration of the laser cutting head, laser power, focal position, pulse pattern, nozzle spacing, gas pressure, beam parameter product/BPP, focal diameter and/or gap width, and

wherein the indirect process variables comprise scattered radiation, gap width, inclination of a cutting edge, temperature distribution in a cutting zone and quality features, including edge roughness, scoring, burr, and/or contour accuracy.

5. The method according to claim 1 , further comprising applying a fast control loop to a first class of quickly controllable parameters, wherein the fast control loop controls the laser cutting process together with the feed rate of the laser cutting head on the basis of currently measured sensor data and/or on the basis of the calculated setpoint data set and/or a setpoint data set corrected on the basis of the sensor data.

6. The method according to claim 5 , wherein the fast control loop and/or a slow control loop are designed as predictive model-based controllers.

7. The method according to claim 1 , further comprising applying a slow control loop to a second class of slowly changing parameters, wherein the slow control loop controls the laser cutting process together with the feed rate of the laser cutting head on the basis of currently measured sensor data and/or on the basis of the calculated setpoint data set.

8. The method according to claim 1 , wherein the process model and/or the machine model is calibrated on the basis of sensor data of the laser cutting process carried out that have been read in and fed back to the respective model.

9. The method according to claim 1 , wherein the first memory and the second memory are integrated together in a common unit.

10. The method according to claim 1 , wherein the process model and the machine model are integrated in a combined model, so that access by the control unit takes place in one step.

11. The method according to claim 1 , wherein setpoint values are continuously calculated from the spatially and time-resolved, combined setpoint data set as a function of a point in time and/or a position on a trajectory.

12. The method according to claim 1 , wherein control of the laser cutting process takes place jointly and in comparison with control of the feed rate of the laser cutting head by means of the spatially and time-resolved, combined setpoint data set, wherein when calculating the spatially and time-resolved, combined setpoint data set, user inputs, which are acquired via a user interface, are taken into account.

13. The method according to claim 1 , wherein the process model and/or the machine model and/or update data are collected on a central server from geographically distributed laser cutting machines for calibrating the process model and/or the machine model.

14. A control unit for calculating a spatially and time-resolved, combined setpoint data set for performing open- and/or closed-loop control of a laser cutting process during laser cutting operation with a laser cutting machine that includes a laser cutting head, with:

a measurement data interface to at least one sensor for measuring sensor data during the laser cutting operation, wherein the sensor data encode a cutting result of the laser cutting process;

the at least one sensor;

a process interface to a first memory, wherein a process model is stored in the first memory, wherein the process model represents the laser cutting process and estimates first status data of the laser cutting process and a cutting result resulting therefrom, wherein the first status data of the laser cutting process include physical laser parameters during laser cutting, wherein the physical laser parameters at least include a feed rate value for the laser cutting head and/or a nozzle spacing value;

a machine interface to a second memory, wherein a machine model is stored in the second memory, wherein the second model represents a kinematic behavior of the laser cutting head during movement thereof and estimates second status data of a movement process and the cutting result resulting therefrom, wherein the second status data of the movement process at least include the feed rate value for the laser cutting head and/or the nozzle spacing value;

a processor configured to execute an algorithm which couples the process model and the machine model via the feed rate value for the laser cutting head and/or via the nozzle spacing value;

wherein the processor is further configured to access the process model via the process interface and the machine model via the machine interface in order, on a basis of the estimated first status data of the laser cutting process and second status data of the movement process, to calculate spatially and time-resolved, combined setpoint data set with coordinated setpoints for the laser cutting process and setpoints for the movement process, taking into account the sensor data, wherein model-estimated cutting results are compared with the measured cutting result, wherein the process model and/or the machine model is updated in an event of deviations and recalculate the spatially and time-resolved, combined setpoint data set; and

wherein the processor is configured to perform the open- and/or closed-loop control of the laser cutting process during laser cutting operation to reduce the deviations.

15. The control unit according to claim 14 , in which the at least one sensor is selected from the group consisting of:

a camera,

a spectral intensity sensor,

a gas pressure sensor,

a gas flow sensor,

a sensor for detecting a laser power, and for detecting a beam shape of the laser beam,

sensors for mechanical subsystems including a sensor for detecting a focal position, a cutting speed, and/or a nozzle spacing,

acceleration sensors for the cutting head, sheet metal and/or machine axes,

temperature probes for detecting a temperature of a cutting gas, a cutting environment, and a workpiece to be cut,

humidity sensors for detecting a humidity of the cutting gas and/or an environment,

sensors for detecting a temperature distribution of melt, and

acoustic sensors.

16. A laser cutting system, further comprising:

the control unit according to claim 14 , and a laser cutting machine, wherein the laser cutting head is movable and wherein the laser cutting head is moved and operated with drives along a geometry according to the setpoint data set calculated by the processor of the control unit.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 17, 2024
From: STEINLIN, MARKUS; HAAS, TITUS
To: BYSTRONIC LASER AG
Reel/Frame 068605/0036 →
Priority Claims (1)
EP 20202334 · Oct 16, 2020 · regional
Continuity (2)
Continuation 18248625
Related Publication 20250001527A1 · Jan 2, 2025
References Cited (29)
US 11467561B2 · Wittwer · 2022 [cited by applicant]
US 20150165549A1 · Beutler · 2015 [cited by applicant]
US 20200130107A1 · Mochizuki · 2020 [cited by applicant]
US 20220043421A1 · Wittwer · 2022 [cited by applicant]
CN 101862909A · 2010 [cited by applicant]
CN 102056705A · 2011 [cited by applicant]
CN 102741009A · 2012 [cited by applicant]
CN 104907700A · 2015 [cited by applicant]
CN 107378232A · 2017 [cited by applicant]
CN 110039190A · 2019 [cited by applicant]
DE 102016104318B3 · 2017 [cited by applicant]
EP 3671373A1 · 2020 [cited by applicant]
EP 4146429B1 · 2024 [cited by applicant]
GB 2378261A · 2003 [cited by applicant]
WO 0139919A1 · 2001 [cited by applicant]
International Search Report and Written Opinion, mailed Feb. 10, 2022, from PCT/EP2021/078619 10 pages. [cited by applicant]
International Preliminary Report on Patentability, mailed Oct. 6, 2022, from PCT/EP2021/078619 13 pages. [cited by applicant]
Kaplan, A F H, “An Analytical Model of Metal Cutting with a Laser Beam”, Journal of Applied Physics, American Institute of Physics, US, vol. 79, No. 5, Mar. 1, 1996, pp. 2198-2208 pp. 12. [cited by applicant]
Wikipedia, “State-space representation”, Dec. 9, 2022 (page last edited) 12 pages. [cited by applicant]
HAAS, Titus, “Set Point Optimisation for Machine Tools”, ETH Zurich Verlag, May 2018 172 pages. [cited by applicant]
N. Lanz, D. et al., “Efficient Static and Dynamic Modelling of Machine Structures with Large Linear Motions,” International Journal of Automation Technology, vol. 12, pp. 622-630, Aug. 7, 2018 pp. 9. [cited by applicant]
J. Zeng, et al., “The Abrasive Waterjet as a Precision Metal Cutting Tool,” 10th American Waterjet Conference, 1999 15 pages. [cited by applicant]
J. Zeng, “Mechanisms of brittle material erosion associated with high-pressure abrasive waterjet processing: A modeling and application study,” Doctoral Thesis, 1992 247 pages. [cited by applicant]
W. Schulz, et al., “Simulation of Laser Cutting”, Springer Netherlands, 2009 49 pages. [cited by applicant]
M. Brügmann, et al., “Optimization of Reactive Gas Laser Cutting Parameters based on a combination of Semi-Analytical modelling and Adaptive Neuro-Fuzzy Inference System (ANFIS),” Lasers in Manufacturing Conference, 201… [cited by applicant]
M. Brügmann, et al., “A theoretical model for reactive gas laser cutting of metals,” Lasers in Manufacturing Conference, 2019 10 pages. [cited by applicant]
Liu et al. “Virtual NC laser cutting machine tool and cutting process simulation”, Proceedings of SPIE 5444, 4th International Conference on Virtual Reality and Its Applications in Industry, Mar. 19, 2004, 7 pages. [cited by applicant]
Chinese First Office Action, mailed Oct. 14, 2023, from Chinese App. No. 202180080759.9, 12 pages. [cited by applicant]
Chinese Decision to Grant, mailed Jan. 12, 2024, from Chinese App. No. 202180080759.9, 3 pages. [cited by applicant]